Method and/or system for recommender system

ABSTRACT

Method embodiments and/or system embodiments are provided that may be utilized to recommend online content to users based, at least in part on a prediction of diffusion of online content through a social network.

BACKGROUND

1. Field

The subject matter disclosed herein relates to a method and/or systemfor recommending online content.

2. Information

Online social networks, such as Facebook and/or Google+, and real-timemicro-blogging platforms, such as Twitter, Tumblr, Weibo and/orInstagram, have proliferated in the last few years. Increased userengagement, such as for social networks and/or micro-blogging platforms,may be correlated to increased revenue for the owner of an online socialplatform (as posted advertisements are more likely to be viewed and/orselected). In addition, if a user may be engaged via a connection to asocial media platform, he or she may contribute feedback to the socialnetwork platform, which may be utilized for profiling and/orpersonalization purposes, for example.

Recommender systems typically generate targeted personalizedrecommendations to facilitate access that may be beyond a user's reach.A user or member may, in some cases, comprise an active entity that alsogenerates additional online content (e.g., through likes,retransmissions, and/or forwards (e.g., retweets and/or reposts)).Current recommender systems may focus on specific users to whichrecommendations are made, which may be disadvantageous at times, andconsequently may not be as focused on other users within a socialnetwork.

BRIEF DESCRIPTION OF DRAWINGS

Claimed subject matter is particularly pointed out and distinctlyclaimed in the concluding portion of the specification. However, both asto organization and/or method of operation, together with objects,features, and/or advantages thereof, it may be best understood byreference to the following detailed description if read with theaccompanying drawing in which:

FIG. 1 s a schematic diagram of a social network graph according to anembodiment;

FIG. 2A is a schematic diagram of another social network graph accordingto another embodiment;

FIG. 2B is the schematic block diagram of a social network graph of FIG.2A;

FIG. 3 is a flow chart of a recommendation process according to anembodiment; and

FIG. 4 is a schematic diagram illustrating a computing environmentaccording to an embodiment.

Reference is made in the following detailed description to accompanyingdrawings, which form a part hereof, wherein Ike numerals may designatelike parts throughout to indicate corresponding and/or analogouscomponents. It will be appreciated that components illustrated in thefigures have not necessarily been drawn to scale, such as for simplicityand/or clarity of illustration. For example, dimensions of somecomponents may be exaggerated relative to other components. Further, itis to be understood that other embodiments may be utilized. Furthermore,structural and/or other changes may be made without departing fromclaimed subject matter. It should also be noted that directions and/orreferences, for example, up, down, top, bottom, and so on, may be usedto facilitate discussion of drawings and/or are not intended to restrictapplication of claimed subject matter. Therefore, the following detaileddescription is not to be taken to limit claimed subject matter and/orequivalents.

DETAILED DESCRIPTION

References throughout this specification to one implementation, animplementation, one embodiment, an embodiment and/or the like means thata particular feature, structure, and/or characteristic described inconnection with a particular implementation and/or embodiment isincluded in at least one implementation and/or embodiment of claimedsubject matter. Thus, appearances of such phrases, for example, invarious places throughout this specification are not necessarilyintended to refer to the same implementation or to any one particularimplementation described. Furthermore, it is to be understood thatparticular features, structures, and/or characteristics described arecapable of being combined in various ways in one or more implementationsand, therefore, are within intended claim scope, for example. Ingeneral, of course, these and other issues vary with context. Therefore,particular context of description and/or usage provides helpful guidanceregarding inferences to be drawn.

With advances in technology, it has become more typical to employdistributed computing approaches in which portions of a problem, such assignal processing of signal samples, for example, may be allocated amongcomputing devices, including one or more clients and/or one or moreservers, via a computing and/or communications network, for example. Anetwork may comprise two or more network devices and/or may couplenetwork devices so that signal communications, such as in the form ofsignal packets and/or frames (e.g., comprising one or more signalsamples), for example, may be exchanged, such as between a server and aclient device and/or other types of devices, including between wirelessdevices coupled via a wireless network, for example.

An example of a distributed computing system is the Hadoop distributedcomputing system, which employs a map-reduce type of architecture. Inthe context the terms map-reduce architecture and/or similar terms areintended to refer a distributed computing system implementation forprocessing and/or for generating large sets of signal samples employinga parallel, distributed process performed over a network of individualcomputing devices. A map operation and/or similar terms refer toprocessing of signals to generate one or more key-value pairs and todistribute the one or more pairs to the computing devices of thenetwork. A reduce operation and/or similar terms refer to processing ofsignals via a summary operation (e.g., such as counting the number ofstudents in a queue, yielding name frequencies). A system may employsuch an architecture for processing by marshalling distributed servers,running various tasks hi parallel, and managing communications andsignal transfers between various parts of the system, in an embodiment.As mentioned, one non-limiting, but well-known example, is the Hadoopdistributed computing system. It refers to an open source implementationof a map-reduce type architecture, but may include other aspects, suchas the Hadoop distributed file system (HDFS). In general, therefore,Hadoop and/or similar terms refers to an implementation scheduler forexecuting large processing jobs using a map-reduce architecture.

In this context, the term network device refers to any device capable ofcommunicating via and/or as part of a network and may comprise acomputing device. While network devices may be capable of sending and/orreceiving signals (e.g., signal packets and/or frames), such as via awired and/or wireless network, they may also be capable of performingarithmetic and/or logic operations, processing and/or storing signals(e.g., signal samples), such as in memory as physical memory states,and/or may, for example, operate as a server in various embodiments.Network devices capable of operating as a server, or otherwise, mayinclude, as examples, dedicated rack-mounted servers, desktop computers,laptop computers, set top boxes, tablets, netbooks, smart phones,wearable devices, integrated devices combining two or more features ofthe foregoing devices, the like or any combination thereof. Asmentioned, signal packets and/or frames, for example, may be exchanged,such as between a server and a client device and/or other types ofnetwork devices, including between wireless devices coupled via awireless network, for example. It is noted that the terms, server,server device, server computing device, server computing platform and/orsimilar terms are used interchangeably. Similarly, the terms client,client device, client computing device, client computing platform and/orsimilar terms are also used interchangeably. While in some instances,for ease of description, these terms may be used in the singular, suchas by referring to a “client device” or a “server device,” thedescription is intended to encompass one or more client devices and/orone or more server devices, as appropriate. Along similar lines,references to a “database” are understood to mean, one or more databasesand/or portions thereof, as appropriate.

It should be understood that for ease of description a network device(also referred to as a networking device) may be embodied and/ordescribed in terms of a computing device. However, it should further beunderstood that this description should in no way be construed thatclaimed subject matter is limited to one embodiment, such as a computingdevice and/or a network device, and, instead, may be embodied as avariety of devices or combinations thereof, including, for example, oneor more illustrative examples.

Likewise, in this context, the terms “coupled”, “connected,” and/orsimilar terms are used generically. It should be understood that theseterms are not intended as synonyms. In most contexts, “connected” isused generically to indicate that two or more components, for example,are in direct physical, including electrical, contact. In this context,“connect(s)” or “connection” refers to a logical or virtual associationbetween two users or members of a social network, which is definedinfra. “Connect” or “connection” also refers to direct and/or indirectcommunication between users or members of a social network. In thiscontext,“coupled” is used generically to mean that two or morecomponents are potentially in direct physical, including electrical,contact; however, “coupled” is also used generically to also mean thattwo or more components are not necessarily in direct contact, butnonetheless are able to co-operate and/or interact. The term coupled isalso understood generically to mean indirectly connected, for example,in an appropriate context.

The terms, “and”, “or”, “and/or” and/or similar terms, as used herein,include a variety of meanings that also are expected to depend at leastin part upon the particular context in which such terms are used.Typically, “or” if used to associate a list, such as A, B or C, isintended to mean A. B, and C, here used in the inclusive sense, as wellas A, B or C, here used in the exclusive sense. In addition, the term“one or more” and/or similar terms is used to describe any feature,structure, and/or characteristic in the singular and/or is also used todescribe a plurality and/or some other combination of features,structures and/or characteristics. Likewise, the term “based on” and/orsimilar terms are understood as not necessarily intending to convey anexclusive set of factors, but to allow for existence of additionalfactors not necessarily expressly described. Of course, for all of theforegoing, particular context of description and/or usage provideshelpful guidance regarding inferences to be drawn. It should be notedthat the following description merely provides one or more illustrativeexamples and claimed subject matter is not limited to these one or moreillustrative examples; however, again, particular context of descriptionand/or usage provides helpful guidance regarding inferences to be drawn.

A network may also include now known,and/or to be later developedarrangements, derivatives, and/or improvements, including, for example,past, present and/or future mass storage, such as network attachedstorage (NAS), a storage area network (SAN), and/or other forms ofcomputing and/or device readable media, for example. A network mayinclude a portion of the Internet, one or more local area networks(LANs), one or more wide area networks (WANs), wire-line typeconnections, wireless type connections, other connections, or anycombination thereof. Thus, a network may be worldwide in scope and/orextent. Likewise, sub-networks, such as may employ differingarchitectures and/or may be compliant and/or compatible with differingprotocols, such as computing and/or communication protocols (e.g.,network protocols), may interoperate within a larger network. In thiscontext, the term sub-network and/or similar terms, if used, forexample, with respect to a network, refers to the network and/or a partthereof. Sub-networks may also comprise links, such as physical links,connecting and/or coupling nodes so as to be capable to transmit signalpackets and/or frames between devices of particular nodes includingwired links, wireless links, or combinations thereof. Various types ofdevices, such as network devices and/or computing devices, may be madeavailable so that device interoperability is enabled and/or, in at leastsome instances, may be transparent to the devices. In this context, theterm transparent refers to devices, such as network devices and/orcomputing devices, communicating via a network in which the devices areable to communicate via intermediate devices of a node, but without thecommunicating devices necessarily specifying one or more intermediatedevices of one or more nodes and/or may include communicating as ifintermediate devices of intermediate nodes are not necessarily involvedin communication transmissions. For example, a router may provide a linkand/or connection between otherwise separate and/or independent LANs. Inthis context, a private network refers to a particular, limited set ofnetwork devices able to communicate with other network devices in theparticular, limited set, such as via signal packet and/or frametransmissions, for example, without a need for re-routing and/orredirecting transmissions. A private network may comprise a stand-alonenetwork; however, a private network may also comprise a subset of alarger network, such as, for example, without limitation, all or aportion of the Internet. Thus, for example, a private network “in thecloud” may refer to a private network that comprises a subset of theInternet, for example. Although signal packet and/or frame transmissionsmay employ intermediate devices of intermediate nodes to exchange signalpacket and/or frame transmissions, those intermediate devices may notnecessarily be included in the private network by not being a source ordestination for one or more signal packet and/or frame transmissions,for example. It is understood in this context that a private network mayprovide outgoing network communications to devices not in the privatenetwork, but such devices outside the private network may notnecessarily be able to direct inbound network communications to devicesincluded in the private network.

The Internet refers to a decentralized global network of interoperablenetworks that comply with the Internet Protocol (IP). It is noted thatthere are several versions of the Internet Protocol. Here, the termInternet Protocol, IP, and/or similar terms, is intended to refer to anyversion, now known and/or later developed of the Internet Protocol. TheInternet includes local area networks (LANs), wide area networks (WANs),wireless networks, and/or long haul public networks that, for example,may allow signal packets and/or frames to be communicated between LANs.The term World Wide Web (WWW or Web) and/or similar terms may also beused, although it refers to a part of the Internet that complies withthe Hypertext Transfer Protocol (HTTP). For example, network devices mayengage in an HTTP session through an exchange of appropriatelycompatible and/or compliant signal packets and/or frames. It is notedthat there are several versions of the Hypertext Transfer Protocol.Here, the term Hypertext Transfer Protocol, HTTP, and/or similar termsis intended to refer to any version, now known and/or later developed.It is likewise noted that in various places in this documentsubstitution of the term Internet with the term World Wide Web (‘Web’)may be made without a significant departure in meaning and may,therefore, not be inappropriate in that the statement would remaincorrect with such a substitution.

Although claimed subject matter is not in particular limited in scope tothe Internet and/or to the Web; nonetheless, the Internet and/or the Webmay without limitation provide a useful example of an embodiment atleast for purposes of illustration. As indicated, the Internet and/orthe Web may comprise a worldwide system of interoperable networks,including interoperable devices within those networks. The Internetand/or Web has evolved to a public, self-sustaining facility that may beaccessible to tens of millions of people or more worldwide. Also, in anembodiment, and as mentioned above, the terms “WWW” and/or “Web” referto a part of the Internet that complies with the Hypertext TransferProtocol. The Internet and/or the Web, therefore, in this context, maycomprise an service that organizes stored content, such as, for example,text, images, video, etc., through the use of hypermedia, for example. AHyperText Markup Language (“HTML”),for example, may be utilized tospecify content and/or to specify a format for hypermedia type content,such as in the form of a file and/or an “electronic document,” such as aWeb page, for example. An Extensible Markup Language (“XML”) may also beutilized to specify content and/or format of hypermedia type content,such as in the form of a file or an “electronic document,” such as a Webpage, in an embodiment. Of course, HTML and/or XML are merely examplelanguages provided as illustrations. Furthermore, HTML and/or XML(and/or similar terms) is intended to refer to any version, now knownand/or later developed of these languages. Likewise, claimed subjectmatter is not intended to be limited to examples provided asillustrations, o course.

As used herein, the term “Web site” and/or similar terms refer to acollection of related Web pages. Also as used herein, “Web page” and/orsimilar terms refer to any electronic file and/or electronic document,such as may be accessible via a network, including by specifying a URLfor accessibility via the Web, in an example embodiment. As alluded toabove, hi one or more embodiments, a Web page may comprise content codedusing one or more languages, such as, for example, markup languages,including HTML and/or XML, although claimed subject matter is notlimited in scope in this respect. Also, in one or more embodiments,application developers may write code in the form of JavaScript, forexample, to provide content to populate one or more templates, such asfor an application. The term ‘JavaScript’ and/or similar terms areintended to refer to any now known and/or later developed version ofthis programming language. However, JavaScript is merely an exampleprogramming language. As was mentioned, claimed subject matter is notintended to be limited to examples and/or illustrations.

As used herein, the terms “entry”, “electronic entry”, “document”,“electronic document”, “content”, “digital content”, “item”, and/orsimilar terms are meant to refer to signals and/or states in a physicalformat, such as a digital signal and/or digital state format, e.g., thatmay be perceived by a user if displayed, played and/or otherwiseexecuted by a device, such as a digital device, including, for example,a computing device, but otherwise might not necessarily be perceivableby humans (e.g., in a digital format).

Likewise, in this context, content (e.g., digital content) provided to auser in a form so that the user is able to perceive the underlyingcontent itself (e.g., hear audio or see images, as examples) is referredto, with respect to the user, as ‘consuming’ content, ‘consumption’ ofcontent, ‘consumable’ content and/or similar terms, “Content may be alsoreferred to as online content.” For example, in embodiments, onlinecontent or an online content item comprises one or more of thefollowing: a video; an image; a web page; an article; a productdescription; a service description; an advertisement; post, tweet,newsfeed, picture, video, or combinations thereof. These terms may beutilized interchangeably.

For one or more embodiments, an electronic document may comprise a Webpage coded in a markup language, such as, for example, HTML (hypertextmarkup language). In another embodiment, an electronic document maycomprise a portion or a region of a Web page. However, claimed subjectmatter is not intended to be limited in these respects. Also, for one ormore embodiments, an electronic document and/or electronic entry maycomprise a number of components. Components in one or more embodimentsmay comprise text, for example, in the form of physical signals and/orphysical states (e.g., capable of being physically displayed). Also, forone or more embodiments, components may comprise a graphical object,such as, for example, an image, such as a digital image, and/orsub-objects, such as attributes thereof, which, again, comprise physicalsignals and/or physical states (e.g., capable of being physicallydisplayed). In an embodiment, content may comprise, for example, text,images, audio, video, and/or other types of electronic documents and/orportions thereof, for example.

Also as used herein, one or more parameters may be descriptive of acollection of signal samples, such as one or more electronic documents,and exist in the form of physical signals and/or physical states, suchas memory states. For example, one or more parameters, such as referringto an electronic document comprising an image, may include parameters,such as time of day at which an image was captured, latitude andlongitude of an image capture device, such as a camera, for example,etc. In another example, one or more parameters relevant to content,such as content comprising a technical article, may include one or moreauthors, for example. Claimed subject matter is intended to embracemeaningful, descriptive parameters in any format, so long as the one ormore parameters comprise physical signals and/or states, which mayinclude, as parameter examples, name of the collection of signals and/orstates (e.g., file identifier name), technique of creation of anelectronic document, purpose of an electronic document, time and date ofcreation of an electronic document, logical path of an electronicdocument (or portion thereof), encoding formats and/or standards usedfor encoding an electronic document, and so forth.

Signal packets and/or frames, also referred to as signal packettransmissions and/or signal frame transmissions, may be communicatedbetween nodes of a network, where a node may comprise one or morenetwork devices and/or one or more computing devices, for example. As anillustrative example, but without limitation, a node may comprise one ormore sites employing a local network address. Likewise, a device, suchas a network device and/or a computing device, may be associated withthat node. A signal packet and/or frame may, for example, becommunicated via a communication channel and/or a communication path,such as comprising a portion of the Internet and/or the Web, from a sitevia an access node coupled to the Internet. Likewise, a signal packetand/or frame may be forwarded via network nodes to a target site coupledto a local network, for example. A signal packet and/or framecommunicated via the Internet and/or the Web, for example, may be routedvia a path comprising one or more gateways, servers, etc. that may, forexample, route a signal packet and/or frame in accordance with a targetand/or destination address and availability of a network path of networknodes to the target and/or destination address. Although the Internetand/or the Web comprises a network of interoperable networks, not all ofthose interoperable networks are necessarily available and/or accessibleto the public.

In particular implementations, a network protocol for communicatingbetween devices may be characterized, at least in part, substantially inaccordance with a layered description, such as the so-called OpenSystems Interconnection (OSI) seven layer approach and/or description. Anetwork protocol refers to a set of signaling conventions, such as forcomputing and/or communications transmissions, as may, for example, takeplace between and/or among devices in a network, typically networkdevices; for example, devices that substantially comply with theprotocol and/or that are substantially compatible with the protocol. Inthis context, the term “between” and/or similar terms are understood toinclude “among” if appropriate for the particular usage and vice-versa.Likewise, in this context, the terms “compatible with”, “comply with”and/or similar terms are understood to include substantial complianceand/or substantial compatibility.

Typically, a network protocol, such as protocols characterizedsubstantially in accordance with the aforementioned OSI description, hasseveral layers. These layers may be referred to here as a network stack.Various types of transmissions, such as network transmissions, may occuracross various layers. A lowest level layer in a network stack, such asthe so-called physical layer, may characterize how symbols (e.g., bitsand/or bytes) are transmitted as one or more signals (and/or signalsamples) over a physical medium (e.g., twisted pair copper wire, coaxialcable, fiber optic cable, wireless air interface, combinations thereof,etc.). Progressing to higher-level layers in a network protocol stack,additional operations may be available by initiating networktransmissions that are compatible and/or compliant with a particularnetwork protocol at these higher-level layers. For example, higher-levellayers of a network protocol may, for example, affect devicepermissions, user permissions, etc.

A virtual private network (VPN) may enable a remote device to moresecurely (e.g., more privately) communicate via a local network. Arouter may allow network communications in the form of networktransmissions (e.g., signal packets and/or frames), for example, tooccur from a remote device to a VPN server on a local network. A remotedevice may be authenticated and a VPN server, for example, may create aspecial route between a local network and the remote device through anintervening router. However, a route may be generated and/or alsoregenerated if the remote device is power cycled, for example. Also, aVPN typically may affect a single remote device, for example, in somesituations. A network may be very large, such as comprising thousands ofnodes, millions of nodes, billions of nodes, or more, as examples.

Online social networks, such as Facebook and/or Google+, and real-timemicro-blogging platforms, such as Twitter, Tumblr, Weibo and/orInstagram, have proliferated in the last few years. Increased userengagement, such as for social networks and/or micro-blogging platforms,may be correlated to increased revenue for the owner of an online socialplatform (as posted advertisements are more likely to be viewed and/orselected). In addition, if a user may be engaged via a connection to asocial media platform, for example, he or she may contribute feedback tothe social network platform, which may be utilized for profiling and/orpersonalization purposes, for example.

Recommender systems typically generate targeted personalizedrecommendations to facilitate access that may be beyond a user's reach.In some situations, for example, a user or member may comprise an activeentity that also generates additional online content (e.g., throughlikes, retransmissions, and/or forwards (e.g., retweets and/orreposts)). Current recommender systems may focus on specific users towhich recommendations are made, which may be disadvantageous at times,and consequently may not be as focused on other users within a socialnetwork, as shall be explained.

In this context, the terms recommender system, recommendation system,recommendation system embodiment, recommender system embodiment,recommendation services, recommendation application, online contentrecommendation tool, online content recommendation system and/or similarterms are, or maybe, used interchangeably. These and/or similar termsrefer to a recommender system that may be employed in a client-servertype interaction, such as a network device and/or a computing device,for example. In an embodiment, a recommender system may be for use inconnection with rendering a GUI via a device, such as a client, forcommunication with a server, for example. A recommender system may alsobe part of a portion of a distributed computing system. In anembodiment, a computing device may interface with a client, which maycomprise features of a client computing device, for example. Acommunications interface, a processor, and a memory may communicate byway of a communication bus, for example. In an embodiment, a clientcomputing device may communicate with a computing device by way of aconnection, such as an internet connection, via a network, for example.Examples are provided merely as illustrations. It is not intended thatclaimed subject matter limited in scope to illustrative examples.

In a general context, social networking is a practice of expanding thenumber of a user's business and/or social contacts by making connectionsthrough individuals or users. In a general context, a social network isa group of individuals that are in direct or indirect communication withother individuals associated with a group. However, in this context, a“social network” comprises and/or refers to users that are logicallyconnected, directly and/or indirectly, to other users, in a network(e.g., computing and communications network). In this context, usersthat are logically connected may directly communicate other users via anetwork utilizing transmissions, such as emails, texts, online content,and/or messages. In an embodiment, users may communicate indirectlythrough retransmissions, forwards, and/or transfers of emails, texts,messages, and/or online content. Users in a social network may bereferred to as “members.” In this content, terms “users” and “members”may be used interchangeably. A social network, for example, may beestablished via software applications and/or web sites, such as Tumblr,Facebook, Twitter, Instagram, LinkedIn, etc. A social network mayinclude a plurality of members, e.g., hundreds, thousands, millions, orbillions of members.

In an embodiment, a social network may comprise at least two members. Inan embodiment, members may have joined a social network and/or beinvited to join a social network. In an illustrative embodiment, forexample, in social network Facebook®, a user may join Facebook® bycreating a profile on a Facebook® mobile software application downloadedonto a user's computing device. Alternatively, a member may create aprofile on a Facebook® web site.

In this context, “members” refers to users, followers, and/or entitiesin a social network that form an association with one or more users,followers, and/or entities in a social network which results in onlinecontent, that is received or requested by a member, for example, beingforwarded and/or shared with another member. In an embodiment, in asocial network, such as, Twitter®, Instagram®, Tumblr®, associatedmembers may be referred to a “followers.” In an embodiment, in a socialnetwork, such as, Facebook, associated members may be referred to as“friends.” In an embodiment, in a social network, such as, LinkedIn,associated members may be referred to as “connections.” All of theforegoing comprise examples of members in this context. In addition,examples of “members” and “associated members” are described below inparagraph [0036].

For example, a member may comprise an active entity that may generateonline content by, for example, expressing opinions about various onlinecontent (e.g., through likes of posts, tweets, videos), including, asexamples, forwards, shares, or retransmissions of viewed online contentto other users (e.g., re-tweets or re-posts).

In an embodiment, within a social network, “associated members” refer tomembers that are logically associated and/or logically connected withother members. In an embodiment, if members are “associated,” a membermay directly or indirectly communicate with an associated member via acomputing, communication, and/or carrier network. In an embodiment,members may be associated with other members via voluntary elections orresponding to communications from other members.

In this context, “social network graph” refers to a subnetwork of alarger social network, such that subnetwork members are capable of or indirect and/or indirect communication with other members. In anembodiment, users or members in a social network graph may berepresented, for purposes of the present application, by “nodes,” andassociations and/or connections with other members may be represented by“edges” and/or “links” that may represent relationships and/orinteractions among associated members. “Edges,” “links,” and/or similarterms, may be utilized interchangeably. In an illustrative embodiment,an edge may connect two nodes in a social network graph, if nodes areassociated by a relationship (e.g., a friend, a follower, a connection,such as ones that may characterize a specific social network, forexample).

For example, FIG. 1 illustrates a schematic diagram of a social networkgraph according to an embodiment 100. In an embodiment, social network,such as 100, may comprise one or more members, such as 102, 104, 106,108, 110, and 112. In an embodiment, members may have joined a socialnetwork and/or be invited to join a social network embodiment, such as100.

In an embodiment, such as illustrated in FIG. 1, members 102 and 108 maybe directly associated, as illustrated by edge 121; members 104 and 106may be directly associated, as illustrated by edge 122; members 108 and110 may be directly associated, as illustrated by edge 123; members 106and 110 may be directly associated, as illustrated by edge 124; andmembers 104 and 112 may be directly associated, as illustrated by edge126. In an embodiment, as illustrated in FIG. 1, members 102 and 110 maybe indirectly associated, as shown by following a path comprising edges121 and 123 from member 102 to member 108 to member 110. Further, asillustrated in FIG. 1, members 104 and 110 may be indirectly associated,as shown by following a path comprising edges 122 and 124 from member104 to member 106 to member 110.

In an embodiment, users and/or members of a social network may becomeassociated, such as by invitation, requests, and/or elections. Forexample, in social network Facebook®, members may invite other membersto be “friends” via, for example, a “friend request.” If members inFacebook® become friends, content or online content posted by one membermay be shared, retransmitted, and/or forwarded to a Facebook® friend(e.g., an associated member). In Instagram®, Tumblr®, and Twitter®,associated members may be referred to as “followers.” In social networkssuch as Instagram®, Tumblr®, and Twitter®, members may be associatedwith another member by “following” another member. In an embodiment,social networks, such as Instagram®, Tumblr®, and Twitter® members mayselect to “follow” another member and a “followed” member may not haveto initially agree to be followed. “Followed” members may “block” afollower, which may disassociate a member from another member. In anembodiment, members of a social network may have to agree to haveanother member associate with them. In a social network such asLinkedIn®, members may become associated through a connection request. Avariety of approaches are possible and intended to be included withinclaimed subject matter.

In this context, “diffusion” or “social network diffusion” refers to arate or measure of online content or content distribution, such asforwarding and/or retransmission among members of a social network. Inan embodiment, a rate or measure of online content forwarding and/orretransmission may comprise an approximate measure. Terms “share,”“sharing,” “forward,” “forwarding,” “retransmit,” “retransmitting,” orother similar terms, may be utilized interchangeably. Referring back toFIG. 1, in an embodiment, if online content, such as a video, isrecommended to member 102, this online content may also be forwarded toassociated members (e.g., members directly associated with member 102,such as member 108, or indirectly associated with member 102, such asmembers 106 or 110). In an embodiment illustrated in FIG. 1, onlinecontent may be directly forwarded and/or retransmitted to member 108.Depending, at least partially, on diffusion parameters (e.g.,retransmission characteristics in a social network graph such as anumber of times online content may be retransmitted), online content mayalso be retransmitted from member 108 to members 106 and/or 110.Further, in an embodiment, online content, originated, in this example,at member 102 may also be forwarded and/or retransmitted from member 106to member 104. Again, a variety of approaches are intended to beincluded within claimed subject matter.

In an embodiment, “diffusion path length” or “path length” refers to anumber of members, links, and/or edges through which online content maybe forwarded and/or retransmitted. Likewise, in an embodiment,“distance” or “path distance” refers to a number of members, links,and/or edges through which online content has been and/or may beforwarded and/or retransmitted. Of course, path length or path distancemay be measured in a variety of ways within claimed subject matter.

Accordingly, in an embodiment, such as illustrated in FIG. 1, onlinecontent may be retransmitted from member 102 to member 108 (through apath comprising edge 121) and then to member 110 (through a pathcomprising edge 123) and then to member 106 (through a path comprisingedge 124). In this example embodiment, a path length (or diffusion pathlength) in a social network graph for this online content may comprisethree (3). In an embodiment, online content retransmitted in a socialnetwork graph from member 104 to member 106 (through a path comprisingedge 122) to member 110 (through a path comprising edge 124) may have adiffusion path length of 2 (e.g., two edges).

Recommender systems may recommend online content to users or members ofa social network. In an embodiment, online content may be forwardedand/or retransmitted from an associated member of a member's socialnetwork. Instead, in an embodiment, a recommender system may review auser or member's search history or web page viewing habits and recommendonline content to a user member.

In an embodiment, diffusion via a social network graph may increase useror member engagement. In this context, an edge (i, j) in a socialnetwork may comprise an edge between associated members i and j (e.g.,if member j follows i). If member j is associated with member i, memberj may view online content retransmitted (e.g., re-tweeted) by member i.If online content is retransmitted, and/or recommended to member i,online content may potentially increase member i's engagement withInternet-type applications and/or a social network, for example,especially if online content is interesting to a member. Continuing withthis illustrative embodiment, if online content is diffused further,e.g., through additional retransmissions (e.g., posts and/or retweets)to members that may be associated with members associated with i and/orj, for example, forwarded online content may increase engagement ofmembers that may be associated with members i and j. For example, in anembodiment, three levels of associated members exist if member j isassociated with member i, member k is associated with member j, and/ormember l is associated with member k. In an embodiment, for example,online content (e.g., a post) may be diffused to two levels ofassociated members if a post is first retransmitted from member i tomember j and then retransmitted from member j to member k. However,current recommender systems may not take into consideration, or may evenoverlook, social network diffusion.

Duplicate receipt of online content by members of social networks mayresult in less efficiency. In an embodiment, online content in a socialnetwork may be recommended to member i and then also may beretransmitted (e.g., re-posted) to member j. Illustratively, in anembodiment, similar online content may also be recommended directly,either by a same source or a separate source, to member j. In thisillustrative example, associated members (e.g., followers and/orfriends) may receive online content through a retransmission as well asa direct recommendation. In an embodiment, recommender systems may havea limited number of recommendation slots or spaces for online contentfor a number of reasons, such as memory or storage considerations, forexample. A limited number of recommendation slots or spaces for a memberin a recommender system may also be based, at least in part, on numbersof members. If a social network has a high number of users, a socialnetwork and/or recommendation system may utilize more computational timeand/or storage space. If members receive online content directly, and/oralso indirectly, such as through a retransmission and/or forwarding ofonline content (e.g., a repost and/or retweet) resources of arecommender system, such as online resources, may be expended onduplicate online content (e.g., tweets and/or retweets) that may haveotherwise already reached associated members (or may otherwise reachthem at a later time). A recommender system may expend excess resourceswith hundreds, thousands, or millions of associated members (e.g.,followers or friends), and if duplicate online content is received byassociated members, for example.

Accordingly, in an embodiment, a recommendation system that takes intoaccount diffusion in a social network may potentially improve userengagement and/or recommender system performance, such as more efficientuse of memory and/or processor resources. As shall be described ingreater detail, in an embodiment, a recommender system may omit, frombeing recommended, online content to members in an online socialnetwork, based at least in part, on an estimate that one or more membersmay be more likely than not to view online content via a differentsource. In addition, in an embodiment, a recommender system mayrecommend non-omitted online content to users based at least in part onpredicted diffusion of online content via a social network. Continuingwith an illustrative embodiment, a recommender system may also recommendnon-omitted content to members until a number of recommendations exceedsa threshold number of recommendations.

In an illustrative embodiment, online content may be diffused tomultiple social network users or members through retransmissions. Thismay potentially lead to increased user engagement, based at least inpart, on exposure to multiple pieces of online content. However,recommender systems, at a present time, may not, or typically may not,identify online content diffusion via a social network. An estimate ofwhether or not users may be more likely than not to otherwise receive,or have received online content may be based, at least in part, on aprediction of diffusion of online content via a social network.

System embodiments may predict diffusion of online content in a socialnetwork utilizing different techniques and/or approaches. In anembodiment, a prediction of diffusion may, for example, be based, atleast in part, on factors such as: online content retransmissionprobability, a number of successive forwards and/or retransmissions,“distance” (e.g., measured in a variety of ways) from a source onlinecontent in a social network graph, and/or online content relevance tomembers or users. For example, a prediction of diffusion may be based,at least in part, on a system generating a set of members that arereachable from a source or an initiating node (e.g., corresponding to amember) in a majority of social network graphs. In this context, a“majority of social network graphs” refers to a situation where,forwarded online content may reach a set of members in a social networkgraph more than a preset number (e.g., 50% or more) of times. In anembodiment, a recommender system may identify groups (e.g., sets) ofmembers (e.g., nodes) that an initiating member may communicate with,either directly or indirectly. In a social network graph, such as FIG.1, for example, this may be illustrated as members that may be connectedvia edges, either directly or indirectly.

In another embodiment, prediction of online content diffusion may bebased, at least in part, on generating a set of associated members witha path distance from an online content source member which is less thana threshold retransmission distance. For purposes of the presentapplication, a “threshold retransmission distance” refers to a largestnumber of edges and/or links that online content may be distributed froma source or initiating node in a social network graph. In an embodiment,if a threshold retransmission distance for online content is two (or twomembers or nodes), then online content may not be retransmitted past twoedges. For example, in an embodiment illustrated in FIG. 1, for example,online content retransmitted from member 104 may be retransmitted twotimes (e.g., from member 104 to associated member 106 and then toassociated member 108). However, member 108 may not retransmit receivedonline content to associated member 102 and/or associated member 110because a threshold retransmission distance has been exceeded (e.g.,more than two edges or members in an example environment). For example,in an embodiment, if a threshold retransmission distance is threemembers or edges, a system may generate a set of members that are lessthan three edges or members from a source member.

In another embodiment, a prediction of online content diffusion may bebased, at least in part, on generating a set of online content that hasa greater probability than a threshold retransmission probability. In anembodiment, a threshold retransmission probability may be based, atleast in part, on a member specific retransmission probability and/ordistance from an originating node (e.g., member) for online content. Inan embodiment, a member specific retransmission probability may becalculated and/or estimated by supplying a member with a number ofonline content items and measuring a number of online content items thata member shared, forwarded and/or retransmitted. In an embodiment, in asocial network, online content may be limited in terms of a number oftimes online content may be forwarded and, thus, a further number ofedges away a node is from an originating node, a lower probability mayexist that a node receives retransmitted online content from anoriginating node.

As noted previously, a recommender system may utilize a social networkgraph to identify and/or capture relationships, associations, and/orconnections between members. In an embodiment, a system may have a set Uof N users that may be connected in a social network graph (G=(V, E)).In this context, connections refer to logical associations between usersin a social network. In this embodiment, V=|U| and E comprises a seriesof directed edges. For example, a directed edge (i, j)

E may exist between members i and j, if j may be associated with i and,thus, views online content generated or retransmitted (e.g., re-posted)by i. In an embodiment, for example, Fi comprises a set or group ofmembers that is associated with i (e.g, Fi={j:(i,j)

E}) and G_(i) comprises a group of members with which i is associated(e.g., G_(i)={j: i

Fj}).

In an embodiment a social network member may receive online content, ina variety of ways, such as through one or more recommended onlinecontent items provided by a recommendation system to member i; and/orthrough retransmissions and/or forwards from associated members (e.g.,retweets, re-posts). In an illustrative embodiment, a system maygenerate recommended online content and/or a recommendation list for amember. For example, L_(i) refers to recommended online content items(or a recommendation list) for member i, S refers to a set of onlinecontent (e.g., posts and/or tweets) present in a system at a presenttime (e.g., online content that has been generated in the past); and Ucomprises a group of members. In this context, a “recommendation list”comprises a number of online content items a recommender system assignsto a member of a social network. “Recommendation list,”“recommendations,” “number of recommendations,” “recommendation items,”“recommendation entries,” and/or other similar terms may be utilizedinterchangeably.

In an embodiment, an online relevance factor (r_(it)) for an onlinecontent item may identify relevance of online content item t for memberi. Illustratively, a relevance factor may identify a degree to which anonline content item t may likely be of interest to member i. A relevancefactor of t may be calculated, for example, by comparing aspects ofonline content t to a history of online content a member has viewed,generated, forwarded, and/or retransmitted in a time window.

In an illustrative embodiment of a recommender system, a member may havea threshold of at most K online content items that a system mayrecommend. This may be referred to as recommendation threshold and maybe represented by |Li|=K. In this illustrative embodiment, K maycomprise less than a total set of online content items S (e.g., posts),which may be represented by K<<|S|.

Continuing with an example embodiment, a recommender system may computean engagement or engagement factor of member i for a social network as arelevance value of online content items t displayed to a member througha recommendation list. In an embodiment, an engagement factor may becorrelated to a relevance factor. For example, in an embodiment,e_(it)=r_(it), (e.g., in an embodiment, an engagement factor may beestimated using a relevance factor as a structured “best guess”,although other types of estimates are contemplated within claimedsubject matter).

In an embodiment, a recommendation system may potentially increasemember engagement by recommending online content that may be relevant toa member. Continuing with an illustrative embodiment, a memberretransmission probability may depend, at least in part, on a member'sprofile as well as relevance of online content to a member. For example,in an embodiment, a member-specific probability of retransmission (e.g.,reposting) may be referred to as w.

As discussed above, online content may be received or requested viarecommendation and/or also via retransmission and/or forwards. In anembodiment, if online content is recommended to a social network member,online content may potentially impact member engagement. In addition, inan embodiment, online content may also potentially indirectly impactother associated members' engagement for example, recommended onlinecontent may be retransmitted to other social network members. Thus,recommendation of online content may result in diffusion in a socialnetwork graph.

In an embodiment, a recommender system may predict (e.g., estimate)diffusion in a variety of manners. Illustratively, in embodiments, arecommender system may utilize different diffusion techniques and/orapproaches, to estimate online content diffusion via a social network.In an embodiment, a recommender system may also determine and/orcalculate engagement potential of online content t in a social networkgraph, after t is initially recommended to user i.

In an embodiment, a recommendation system may utilize member-specificretransmission probability and generate a probabilistic graph G^(i),where edges (i,j)

E may occur with a member-specific retransmission probability w_(i). Inan embodiment, a recommendation system may generate a set of nodes(which correspond to members) reachable (or at least potentially orbelieved to be reachable) from node i in a majority of social networkgraphs. In this context, a probabilistic graph comprises a socialnetwork graph comprising edges (or paths between members) with assignedmember-specific retransmission probability values. In an embodiment,illustratively, if online content t is recommended to member i, member jis considered reachable from i if a path exists between i and j. In anillustrative embodiment, a probability of existence of a path comprisesa product of retransmission (repost) probabilities of nodes (e.g.,representing members) across a path except for j. Continuing with anillustrative embodiment, a recommender system may generate a reliablenode set (e.g., R_(i)) for member i, which may be utilized to computeengagement potential for online content t. In this context, a “reliablenode set” R_(i) comprises a group and/or number of nodes which arereachable from member i with a probability greater than a thresholdprobability n. In this context, e_(jt) refers to an engagement factorfor online content t and member j. In this context, E^(l) _(it)represents an engagement potential for online content t if recommendedto member utilizing a diffusion technique I. Thus, in an exampleembodiment, a recommender system may generate an engagement potentialfor online content t which is recommended to member i substantially inaccordance with the following:

$E_{it}^{I} = {\sum\limits_{j \in {R\; i}}\; e_{jt}}$

In this context, Σ refers to summing a number of online content itemsand as shown above here, in particular, summing engagement factors formembers j in a reliable node set. In an illustrative embodiment, areliable node set (R_(i)) of i may be estimated utilizing Monte Carlosampling. In this context, Monte Carlo sampling refers to choosing alarge number of independent-variable samples at random from within aninterval or region, and averaging, including weighted averages, theresulting dependent-variable values. Thus, dividing by a span of aninterval or a size of a region over which random samples were chosen mayalso be employed in some situations. In Monte Carlo sampling, arecommendation system embodiment may sample a set D of deterministicgraphs from an original (e.g., initial) probabilistic graph P byconsidering edge probabilities and, for deterministic graphs, arecommendation system embodiment may calculate and/or compute a set ofnodes reachable from node i, which comprises a reliable node set (Ri).For example, in an illustrative embodiment, a recommender systemembodiment may compute Ri in an amount of time, proportional toO(|D|(|V|+|E|)).

In this context, a “diffusion subgraph” comprises a section orsubnetwork of a social network graph identifying paths, links, and/oredges and nodes (e.g. members) to which online content may beretransmitted, or forwarded. Utilizing a second technique for predictingdiffusion of online content in a social network, in another embodiment,a recommender system may recommend online content t to member i and amember may retransmit t to create a diffusion subgraph G^(II) _(i) whosesource node is member i. Thus, in an example embodiment, a recommendersystem may retransmit online content to an associated member. However,in an embodiment, a recommender system may limit a retransmission ofonline content to a consecutive number of times, e.g., r number oftimes. Further, in an illustrative embodiment, r may reflect thatrelevance of online content may decrease, such as, linearly orexponentially with time, and that retransmissions (e.g., reposts) ofonline content may consequently cease after a specified number of times.

In an embodiment, a diffusion subgraph G^(II) _(i) may include nodes(e.g., corresponding to members) j for which a shortest path from memberi to member j is at most r. In this context, an engagement potential foronline content t initially recommended to member i comprises a sum ofengagement factors for nodes in a diffusion subgraph G^(II) _(i). Inthis context, e_(jt) refers to an engagement factor for online content tand member j. In this context, E^(II) _(it) comprises a value for anengagement potential for online content t if recommended to member iutilizing a diffusion technique, e.g., a diffusion technique such asdescribed immediately above. In this context, j

G^(II) _(i) refers to users j in a diffusion subgraph. Thus, in anillustrative embodiment, a recommender system may generate an engagementpotential for online content t initially recommended to member isubstantially in accordance with the following:

$E_{it}^{II} = {\sum\limits_{j \in G_{i}^{II}}\; e_{jt}}$

In an embodiment, a summation above (e.g., Σ) comprises summingengagement factors for users j in diffusion subgraph G^(II) _(i). In anexample embodiment, member j receive online content through severalmembers that member j is associated with (e.g., is a “follower” of, is“friends” with). However, a recommender system embodiment calculatingengagement potential may count a member a single time, and not multipletimes, so as to not skew an engagement potential as a result of same orsimilar online content being received from a number of members, forexample.

In yet another embodiment for predicting diffusion, a recommender systemembodiment may forward online content viewed by member i to members j aslong as a retransmission (re-post or retweet) probability w_(j) ofmembers exceeds an online content retransmission threshold Θ. Forexample, a retransmission probability for node (e.g., corresponding to amember) j may comprise w_(j)f(r) where f(r) comprises a non-increasingfunction of distance r and w_(j) comprises member j's specificretransmission (e.g., re-post and/or retweet) probability. In anillustrative embodiment, a shortest path from a source (e.g.,initiating) member to member j comprises r. Thus, for example, if amember is at distance r from a source (e.g., initiating) member, and aretransmission probability is less than threshold Θ, a recommendationsystem embodiment, as is discussed previously, may not further forwardonline content. Continuing, a recommender system embodiment may generatea directed subgraph G_(i) ^(III) whose source member comprises member i.In an embodiment, an engagement potential for online content t comprisesa sum of engagement factors for members in subgraph G_(i) ^(III). Inthis context, E^(III) _(it) comprises a value for an engagementpotential for online content t if recommended to member i utilizinganother diffusion technique, e.g., a diffusion technique, such asdescribed immediately above. In this context, j

G^(III) _(i) refers to members (or users) in a diffusion subgraphdiscussed immediately above. Thus, in an illustrative embodiment, arecommendation system embodiment may generate an engagement potentialfor online content t recommended to member i in accordance with thefollowing:

$E_{it}^{III} = {\sum\limits_{j \in G_{i}^{III}}\; e_{jt}}$

In an embodiment, a summation above (e.g., Σ) comprises summingengagement factors for users j in diffusion subgraph G^(III) _(i).

In an embodiment, a recommendation system may generate recommendationsto social network members by predicting (e.g., estimating) diffusion ofonline content in a social network. In an embodiment, a recommendationsystem may provide recommendations to increase member engagement by notrecommending online content that may be expected to otherwise reachsocial network users through diffusion. For example, if an onlinecontent item is recommended, by a recommendation system embodiment, to amember without taking into account social network diffusion, similaronline content could be recommended, by a recommender system embodiment,to one or more users that may have already received (or may laterreceive) similar online content through diffusion. This may result inredundancy and/or less efficiency in online content avocation sincemembers may receive online content through retransmission as well asdirectly through recommendation. However, in an embodiment, if care istaken so that members who may likely receive online content throughretransmission do not receive recommendations from a recommendationsystem embodiment for similar online content, social network members maypotentially have higher user engagements. In an embodiment, arecommender system may generate a set of online content items formembers, so as to increase user engagement. Accordingly, in anembodiment, a recommendation system may potentially increase userengagement by taking into consideration that a limited number ofrecommendations may be made due to system resources (e.g., space inmemory and/or processing resources) and that similar online content maynot be assigned to members linked by an edge, for example, in a socialnetwork graph.

In an embodiment, a recommendation system may initially identify aselected group of members to increase online content diffusion and/oruser engagement. In this context, a “selected group” or a “selected set”refers to members that a recommender system identifies as havingcharacteristics desirable for improved content diffusion. For example, a“selected group” of members may have characteristics, such as, membershaving no associated members and/or members having a high diffusion rate(e.g., a high number of associated members). “Selected set,” “selectedgroup,” “selected member group,” “selected member set,” “independentmember set,” “independent member group,” “selected set of members,”“selected group of members,” and/or other similar terms, may be usedinterchangeably. In an embodiment, a recommendation system to increaseonline content diffusion may not assign and/or recommend similar onlinecontent to two or more members if two or more members are associatedmembers because associated members would likely already receive onlinecontent through a retransmission.

Continuing with an illustrative embodiment, a recommendation system maynot generate a selected group of members to include two or more membersthat are “associated members” because online content recommended to onemember may be forwarded and/or retransmitted to an “associated member.”For example, in an embodiment, in a social network graph G_(n)(V_(n),E_(n)), V_(n) may represent members (nodes) and E_(n) mayrepresent edges (e.g., links and/or associations). In a social networkgraph, members i and j may be associated, for example, if node j belongsin a diffusion subgraph of member i. For example, a recommendationsystem embodiment may not assign and/or recommend similar online contentto two members (e.g., i and j) if connected nodes are linked by an edgein a social network graph (e.g., or are associated, such as “friends” inFacebook; “followers” in Tumblr, Instagram, and/or Twitter; and“connections” in LinkedIn, etc.).

A recommendation system embodiment may recommend online content t tomember i. In an embodiment, recommendation of online content t to memberi may be represented as x_(it). In an illustrative embodiment, just asan example, an online content recommendation x_(it) may be assigned avalue of 1 if x_(it) is recommended, or may be assigned a value of 0 ifx_(it) is not recommended.

Continuing with an illustrative embodiment, a recommendation system maypotentially increase user engagement within a social network graph, suchas by implementing constraints such as a) a limited number (K) of onlinecontent (e.g., posts) being assigned and/or recommended to a member(based, at least in part, on limited resources in an recommender system)and/or b) online content not being assigned and/or recommended to anynodes (e.g., members) that are linked with an edge in a social networkgraph, (e.g., cannot assign similar online content to associatedmembers).

In this context, “max x” may refer increasing a measurement of userengagement. Un this context,

$\sum\limits_{i = 1}^{N}$

comprises summing and/or calculating for members i through N. In thiscontext,

$\sum\limits_{t \in S}\; {E_{it}x_{it}}$

comprises a product of an engagement factor E_(it) (e.g., engagementfactor for online content t for user i) and recommendation x_(it) (e.g.,which may be set as a value of 0 or 1) summed for online content items tin a group of S posts.

Thus, in an example embodiment, a recommendation system may potentiallyincrease user engagement for online content t recommended to member isubstantially in accordance with the following:

$\max\limits_{x}{\sum\limits_{i = 1}^{N}{\sum\limits_{t \in S}\; {E_{it}x_{it}}}}$

subject to constraints, substantially in accordance with the following:

Constraint (a)

${{\sum\limits_{t \in S}\; x_{it}} = K},$

-   -   which references that a number of online content recommendations        made to a member is bounded and/or limited by number K.

Constraint (b),

x _(it) +x _(jt)≦1, ∀ (i, j) ∈ E _(n) , ∀t,

-   -   which references that similar online content (e.g., x_(it) +x        _(jt)) may not be assigned (e.g., recommended) to nodes (e.g.,        members) if nodes are linked with an edge. In other words, if        members are associated, then online content t is not to be        assigned to both members i and j. For example, if similar online        content was assigned to members i and j, x_(it) and x_(jt) would        both be set to 1, the sum x_(it)+x_(jt) would be greater than 1,        and, therefore, would exceed this constraint.

However, computing and/or generating a selected group of members may beresource intensive and may, consequently, not be practical and/ortechnically feasible under some circumstances. In an embodiment, arecommendation system may determine or compute engagement weights foronline content item t (e.g., post) by, determining possible member sets,and by computing engagement weights for those possible member sets, and,thus, identify an selected member set X_(i), with an increasedengagement weight. For example, in an embodiment, a selected member setX_(i) may comprise a relatively high engagement weight relative to othermember sets. Thus, in an embodiment, a recommendation system determininga potential engagement weight in this manner, may consume significanttime and/or resources in performing such a calculation and/ordetermination based at least at part on calculations being performed toidentify a member set with an increased engagement weight. Accordingly,a different method or process may be desirable to more efficientlyutilize recommendation system resources.

In an embodiment, a recommendation system may utilize a process (e.g.,denoted IS (G_(n),t)) to more efficiently utilize recommendation systemresources and to identify a selected set of members X_(i) with arelatively high potential engagement weight. In an embodiment, processIS(G_(n),t) may receive neighborhood graph G_(n) and online content t(e.g., post), and may generate a selected set of members X_(i) of weightWX_(i).

In an embodiment, for example, a recommendation system may sort members(e.g., nodes) in a decreasing order using one or more parameters forordering, for example. In an embodiment, for process IS(G_(n), t), arecommendation system may likewise parse members (e.g., nodes)sequentially in a forward direction without performing backwardscorrections even if parsing order is not precisely correct. This mayreduce recommendation system resources (e.g., computational timeexpended).

Continuing with an illustrative embodiment a recommendation system mayarrange members (e.g., nodes) in a decreasing order by a ratio ofengagement weight compared to “out-degree” (e.g., E_(it)/(1+d_(i))),where engagement weight is determined by a particular diffusion approachfor the particular embodiment. In this context, d_(i) refers to a member(e.g., node) “out-degree” of a member (e.g., node). In terms of a graph,“out-degree” comprises a number of linked nodes that a node has in asocial network graph. In other words, in an embodiment, “out-degree”refers to a number of associated members for a particular member.

Continuing with an illustrative embodiment, in process IS(G_(n),t), arecommendation system may increase engagement weight by selectingmembers, for a selected member set, with large engagement weight (e.g.,members for which recommendation of online content t could lead toincreased diffusion of online content into a social network) and/orselecting members, for a selected member set, with a small out-degree(e.g., members which may not receive online content through diffusion).In other words, a recommender system embodiment may select a member(e.g., node) to be in a selected group of members (e.g., X_(i)) ifselecting the member increases the number of associated members of theselected group so that online content may be diffused throughretransmission and/or forwarding (e.g., diffused to a large number ofassociated members). Additionally, a recommender system embodiment mayselect a member with a small out-degree to be in selected group ofmembers X_(i) because selecting the member with a small out-degreeindicates the member (e.g., node) likely may not have received onlinecontent through retransmission and/or forwarding.

Continuing with an illustrative embodiment, in process IS(G_(n),t), arecommendation system may also remove members (e.g., nodes) fromselected group of members X_(i). In an embodiment, a recommendationsystem may remove members that may, more likely than not, receive onlinecontent through diffusion (e.g., retransmission and/or forwarding). Inan embodiment, in terms of a graph, F_(i) comprises a set of nodeslinked to node i (e.g., members that are associated with member i). Inan embodiment, such as social network Facebook, F_(i) may comprise“friends” of user i and in social network Twitter and/or Instagram,F_(i) may comprise “followers.” For example, in an embodiment, if amember i is included in a selected group of members X_(i), members thatare associated to member i may be removed from a selected group ofmembers, such as X_(i), and in addition, in terms of a graph, outgoinglinks of nodes in F_(i), (e.g., i's out neighbors, “associated members,”“friends,” and/or “followers”) may be removed from selected group ofmembers X_(i). In other words, in an embodiment, a recommendation systemmay prioritize members that have potential to increase diffusion ofonline content, such as members that may increase retransmission (e.g.,repost), and/or members with small out-degree (e.g., for a graph, nodesthat do not have a large set of conflicting neighbor nodes to whichsimilar online content (e.g., posts or tweets) may be assigned).

For the discussion below of the graphs of FIGS. 2A and 2B and for thepseudocode descriptions, nodes comprise members, links compriseassociations, and recommendation of online content comprises assignmentto a node, such as has been described previously. Thus, without loss ofgenerality, terms may be interchanged below for purposes of discussionand/or comprehension. Thus, as example, the term ‘nodes’ may be used torefer to members, the terms “links” may be used to refer to associationsbetween members, etc. In an embodiment below, process IS(G_(n),t) isillustrated in pseudocode. In an illustrative embodiment, recommendationsystem may generate a selected member set and/or group of nodes and/ormembers (e.g., I_(t)) so as to increase member engagement weight. Inthis context, an engagement weight (e.g. relevance) of a selected groupof nodes may be represented by W(I_(t)). In an embodiment, a group ofnodes I_(t) may be identified for online content t recommended to memberi substantially in accordance with a following process or method:

-   Input Variables: Social network graph Gn, online content t,    engagement weight Eit ∀i ∈ Vn.-   Output Variables: Selected Member Group It for post t of weight    W(It). Post t is recommended to members i ∈ It.

Initialization: It ← Ø ; W(It) = 0 ; V ′n← Vn ; E′n ←En. Sort nodes i indecreasing order of Eit/(1 + di).  While Vn= Ø do Examine nodes i ∈ Vnin the order they are sorted above.  if It ∪ {i} is an independent setthen It ← It ∪ {i}  W(It) ← W(It) + Eit Vn ← Vn \ {i} \ Fi En ← E′n \{(i, j) ∈ En : j ∈ Fi} end if  end while

Continuing with an illustrative embodiment, a recommendation system maygenerate a selected group of members (e.g., I_(t)) and may generate Konline content items (e.g., posts) to recommend to a member i. In anembodiment, a recommendation system may order (e.g., arrange or sort) Konline content items in a decreasing order of engagement weight and/orrelevance, e.g., w(I_(t)). In an embodiment, for example, K onlinecontent items may be arranged from a higher engagement weight to a lowerengagement weight. Continuing with an illustrative embodiment, arecommender system may assign online content items to a selected groupof members in a decreasing order of weight. In an embodiment, arecommender system may assign online content to members until anagreed-upon number, or a threshold number of online content items, hasbeen reached. In another embodiment, a recommender system may assignonline content until a selected group of members (e.g., X_(i) or I_(t))is exhausted, and therefore, may not receive additional online content.An overall time for generating and sorting posts, according to theprocess embodiment outlined immediately above, for example, may becomputed in an amount of time, proportional to O(|S|(|Vn|+|En|+log|S|)). In an embodiment, computing time may be measured by the time to:(i) execute a process IS (G_(n),t) to generate of a selected group ofmembers for online content (e.g., where t is a post in a group of posts,e.g., t ∈ S), which may consume a duration that is linearlyproportional, for example, to a size of a neighborhood graph, and (ii)execute a procedure to sort online content (e.g., posts) in S.

Likewise in an example embodiment, a process for generatingrecommendations or a recommendation list is illustrated in pseudocodebelow. In an embodiment, a system may potentially generaterecommendations resulting in higher potential engagement for a group ofmembers in accordance with the following:

-   Input Variable Set U(=Vn) of users/members, set S of posts, social    network graph Gn, Engagement metric Eit ∀i ∈ Vn, ∀t ∈ S.-   Output Variable: Recommendation of a group Li of K posts for each    user i.

Initialization: For i ∈ Vn do Li ← Ø end for Vn ← Vn ; S′ ← S for t ∈ Sdo IS(Gn, t) end for Sort posts t in decreasing order of IS weight W(It)while Vn 6= Ø and S′ 6= Ø do  Consider posts t in the order they aresorted above for i ∈ It do Li ← Li ∪ {t} if |Li| = K then Vn ← Vn \ {i}end if end for  S′ ← S′ \ {t} end while

As an illustration of how diffusion in a social network may potentiallyincrease relevance and/or member engagement, a recommendation systemembodiment may operate in accordance with the following. In anillustrative embodiment, assume a social network comprises two members,e.g., members 1 and 2. In this embodiment, a recommendation system maygenerate a recommendation or recommendations for members. For purposesof the present application, r_(t) may comprise online content relevance.For purposes of the present application, r_(it) may comprise onlinecontent relevance (e.g., engagement) for member i for online contentitem t (e.g., a post). In an embodiment, relevance of online contentitem 1 for member 1 may have a same or similar value as relevance ofonline content item 1 for member 2. In addition, relevance of onlinecontent item 2 for member 1 may have a same or similar value asrelevance of online content item 2 for member 2. In an embodiment,relevance for online content item 1 may be greater than relevance foronline content item 2 so a recommendation system may recommend onlinecontent item 1 to both members. In an embodiment, online content t₁ maybe recommended to member 1 and member 2, and member 1 may retransmit t₁so that member 2 receives two copies of t₁ (one copy via recommendationand one copy via retransmission). Thus, a combined relevance may be 2r₁.

In another embodiment, a recommender system may assume online contentmay be retransmitted. In an embodiment, for example, online content t₁may be recommended to member 1, online content t₂ may be recommended tomember 2, and online content t₁ may be retransmitted (e.g., reposted) tomember 2. In an illustrative embodiment, member 2 may therefore receivetwo relevant online content items. Thus, a combined relevance for asocial network comprising member 1 and member 2 is 2r₁+r₂, which isgreater than 2r₁, above. Accordingly, a recommender system embodimentwhich takes into account social network diffusion of online content maypotentially improve a relevance of online content viewed by members.Further, member engagement may be potentially increased.

In another embodiment, assume an illustrative social network comprisesfive users and five online content items are available forrecommendation. As mentioned previously, members and users may be usedinterchangeably with regard to social networks, in this embodiment, forexample, users 2 and 3 may be associated with (e.g., “follows”) user 1.Users 4 and 5 may be associated with (e.g., “follows”) user 3. FIG. 2Ais a schematic diagram of a social network graph 200 comprising fiveusers accordingly to an embodiment. In an illustrative embodiment, forexample, a relevant online content item (e.g., post or tweet) may berecommended and/or assigned to a user and a recommended and/or assignedonline content item may be retransmitted (e.g., re-posted or retweeted)to a member that is associated with (e.g., “follows”, is “friends” with)another user. In this example embodiment, for example, relevance (e.g.,engagement weight) of online content t₁, t₂, t₃, t₄, and t₅ (e.g., postsor tweets) for a user may be assigned as follows:

User 1 (relevance of t₁, t₂, t₃, t₄, and t₅) (0.9, 0.1, 0,3, 0.7. 0.4)

User 2 (0.8, 0.6, 0.4, 0.3, 0.5)

User 3 (0.5, 0.7, 0.2, 0.1, 0.9)

User 4 (0.4, 0.8, 0.7, 0.1, 0.3)

User 5 (0.7, 0.1, 0.5, 0.7, 0.4)

Continuing with the illustrative embodiment, a recommender system mayassign a highest relevant post to a user. Thus, as highlighted in boldabove, a recommender system embodiment may recommended online content t₁205 to user 1 210, online content t₁ 205 may be recommended to user 2220, online content t₅ 206 may be recommended to user 3 230, onlinecontent t₂ 207 may be recommended to user 4 240, and online content t₁205 may be recommended to user 5 250. In an embodiment, a recommendersystem may calculate combined relevance by adding relevance ofrecommended online content and relevance or retransmissions of onlinecontent (e.g., posts and reposts). Therefore, for FIG. 2A, a combinedrelevance for users may be identified as follows:

-   -   User 1 210=0.9:    -   User 2 220=0.8 (note that although user 2 received t₁ 205 via        recommendation and retransmission, it is counted once)    -   User 3 230=1.4 (t₁ 205 through retransmission+t₅ 206 through        recommendation)    -   User 4 240=1.2 (t₁ 205 through retransmission+t₂ 207 through        recommendation)    -   User 5 250=0.7 (t₁ 205 through recommendation and        retransmission).

Thus, a combined relevance of online content for user 1 210, user 2 220,user 3 230, user 4 240, and/or user 5 250 above is 5.0 by addingrelevance of online content for user 1 210, user 2 220, user 3 230, user4 240, and/or user 5 250.

In an alternative embodiment, a recommendation system may take intoconsideration online content diffusion and/or relevance of onlinecontent. In an illustrative embodiment, a recommendation system mayexclude or omit online content (e.g., posts) from being recommended tousers if the online content a) has been previously assigned to usersand/or b) may reach a user through retransmissions (e.g., re-posts,retweets).

FIG. 2B is the schematic block diagram of a social network 200 of FIG.2A. In an embodiment, a recommender system may assign online content t₁205 to user 1 210. In an embodiment, online content t₁ 205 may reachusers 2 220 through 5 250 through retransmissions, which results in acombined relevance of 3.3 (0.9+0.8+0.5+0.4+0.7) for user 1 210. In anembodiment, a recommender system embodiment may assign user 2 220 a nextrelevant online content item (e.g., post t₃ 209), which may also beretransmitted (e.g., re-posted to user 4 240 and user 5 250), and thisresults in a combined relevance rating of 1.6 (0.4+0.7+0.5) for user 2220. Continuing, a recommender system embodiment may assign user 3 230 anext relevant online content item (e.g., post) that user 3 230 has notalready received through retransmission (e.g., repost), which may be t₅206, and this leads to a relevance rating of 0.9 for user 3 230.Continuing, a recommender system embodiment may assign user 4 240 a nextrelevant online content item (e.g., post) that user 4 240 has notreceived through retransmission which may be t₂ 207 (e.g., t₁ 206 and t₃209 have been received through retransmission) and this leads to arelevance rating of 0.8 for user 4 240. Continuing with thisillustrative embodiment, a recommender system may assign user 5 250 anext relevant online content item that user 5 250 has not receivedthrough retransmission, which is t₄ 208 and this leads to user 5 250having a relevance of 0.7. In an embodiment, a recommendation system maycalculate a combined relevance for a social network graph 200 comprisingfive users, as is illustrated, for example, in FIG. 2B, by addingrelevance values for each user to result in a combined relevance of 7.3.Here, a combined relevance of 7.3 is an increase of 46% over thepreviously disclosed recommender system embodiment that did not takeinto consideration social network diffusion of online content.

In an embodiment utilizing social network Tumblr, a set of 1.5 millionusers was sampled, and public online content (e.g., posts) were searchedand/or reviewed. In an embodiment, a recommender system determined orcalculated repost probability. Continuing with an illustrativeembodiment, a recommender system generated social network graphs for aset of users. A recommender system identified a set of users that werereachable from a source node with a known probability and a recommendersystem computed and/or estimated a probability of reposts. In anembodiment, a recommender system generated member engagement utilizing adiffusion process, such as an embodiment described herein, and comparedmember engagement to a content type recommender system (e.g., a systemwhere a member's history and personalization may be utilized), and/oralso an existing Tumblr recommendation system. Table 4, below, providesresults of such as comparison and illustrates how a recommender systemembodiment utilizing a diffusion process embodiment outperformed acontent type recommender system and/or an existing Tumblr recommendationsystem. Table 4 illustrates that an average member engagement for arecommendation system embodiment using a diffusion process embodimentincreases from 129% improvement to 177% improvement to 191% improvementas a number of recommended online content items (e.g., posts) increaseor as recommendation online content/post threshold values increases. Inan embodiment, a recommender system embodiment utilizing a diffusionprocess embodiment may more efficiently exploit recommendation systemresources better by omitting, from recommendation, online content thatmay more likely than not reach members from other sources. In anembodiment, a recommender system utilizing a diffusion processembodiment may recommend different online content, which may potentiallybe more relevant to a member and thus may increase member engagement.

K 1 5 10 15 20 25 Content 2444 2368 2298 2242 2196 2156 Social 4395 41173912 3753 3619 3498 Real 3385 3395 3404 3399 3398 3398 DiffRec 1008011414 10831 10504 10421 10185 +129% +177% +177% +180% +188% +191%

Thus, in an embodiment, a recommendation system may generaterecommendations that account for online content diffusion in a socialnetwork. FIG. 3 is a flow diagram of an embodiment of a process togenerate a recommendation list of online content. Of course, embodimentsare intended to be illustrative examples rather than be limiting withrespect to claimed subject matter. Likewise, for ease of explanation, anembodiment may be simplified to illustrate aspects and/or features in amanner that is intended to not obscure claimed subject matter throughexcessive specificity and/or unnecessary details. Embodiments inaccordance with claimed subject matter may include all of, less than, ormore than blocks 305-310. Also, the order of blocks 305-310 is merely asan example order.

Referring to FIG. 3, at block 305, a recommendation system may omitonline content from being recommended to one or more members in anonline social network, based at least in part, on a computation that oneor more members are more likely than not to view online content via adifferent source. In an illustrative embodiment, for example, thecomputation may be based, at least in part, on a prediction orcalculation of diffusion of online content through a social network.Continuing with this embodiment, a prediction or calculation ofdiffusion may be based on one or more of the following factors: onlinecontent retransmission probability; a number of successiveretransmissions; distance from an online content source location in asocial network graph; and/or relevance of online content to a member. Atblock 310, in an embodiment, a recommender system may recommend onlinecontent to members based, at least in part, on predicted diffusionweight of online content. Continuing with this illustrative embodiment,a recommender system may recommend online content to members untilrecommendation slots are filled, and/or a recommendation threshold valueis reached. For example, in an embodiment, a recommender system mayallot member y with space (e.g., recommendation slots) for two onlinecontent recommendations and the recommender system may recommend up totwo online content items.

For purposes of illustration, FIG. 4 is an illustration of an embodiment400 of a system that may be employed in a client-server typeinteraction, such as described infra, in connection with rendering a GUIvia a device, such as a network device and/or a computing device, forexample, a client computing device, such as 402, may comprise a mobilecomputing device with a processor. A mobile computing device may includeexecutable instructions stored in a memory. Stored instructions may beexecutable, such as by a computing device, as previously suggested. Forexample, a user may use a device, such as 402, to login to a socialnetwork, such as Facebook, Tumblr, Twitter, Weibo, and Instagram. Inorder to increase the user's engagement with the social network andrecommend relevant posts, instructions may be stored locally andexecutable to omit, from being recommended, to one or more users in anonline social network based at least in part on an estimate that the oneor more users are more likely than not to view the online content via adifferent source. In addition, instructions may be stored locally tofurther comprising recommending non-omitted online content to usersbased, at least in part, on predicted diffusion weight of the onlinepost. Alternately, electronic signal transmissions may be initiatedacross a network, such as 408, to a second device, such as 404, whichmay comprise a server. Thus, server 404, for example, may include storedexecutable instructions to generate a route recommendation that may becommunicated back to device 402, for example. It is, of course, notedthat device 404 may comprise more than one server as well,

In an illustrative example, In FIG. 4, computing device 402 (‘firstdevice’ in figure) may interface with client 404 (‘second device’ infigure), which may comprise features of a client computing device, forexample. Communications interface 430, processor (e.g., processing unit)420, and memory 422, which may comprise primary memory 424 and secondarymemory 426, may communicate by way of a communication bus, for example.In FIG. 4, client computing device 402 may represent one or more sourcesof analog, uncompressed digital, lossless compressed digital, and/orlossy compressed digital formats for content of various types, such asvideo, imaging, text, audio, etc. in the form physical states and/orsignals, for example. Client computing device 402 may communicate withcomputing device 404 by way of a connection, such as an internetconnection, via network 408, for example. Although computing device 404of FIG. 4 shows the above-identified components, claimed subject matteris not limited to computing devices having only these components asother implementations may include alternative arrangements that maycomprise additional components or fewer components, such as componentsthat function differently while achieving similar results. Rather,examples are provided merely as illustrations. It is not intended thatclaimed subject matter be limited in scope to illustrative examples.

Memory 422 may be representative of any storage mechanism. Memory 420may comprise, for example, primary memory 422 and secondary memory 426,additional memory circuits, mechanisms, or combinations thereof may beused. Memory 420 may comprise, for example, random access memory, readonly memory, etc., such as in the form of one or more storage devicesand/or systems, such as, for example, a disk drive, an optical discdrive, a tape drive, a solid-state memory drive, etc., just to name afew examples. Memory 420 may be utilized to store a program. Memory 420may also comprise a memory controller for accessing computerreadable-medium 440 that may carry and/or make accessible content, whichmay include code, and/or instructions, for example, executable byprocessor 420 and/or some other unit, such as a controller and/orprocessor, capable of executing instructions, for example.

Under direction of processor 420, memory, such as memory cells storingphysical states, representing, for example, a program, may be executedby processor 420 and generated signals may be transmitted via theInternet, for example. Processor 420 may also receive digitally-encodedsignals from client computing device 402.

Network 408 may comprise one or more network communication links,processes, services, applications and/or resources to support exchangingcommunication signals between a client computing device, such as 402,and computing device 406 (‘third device’ in figure), which may, forexample, comprise one or more servers (not shown). By way of example,but not limitation, network 408 may comprise wireless and/or wiredcommunication links, telephone and/or telecommunications systems, Wi-Finetworks, Wi-MAX networks, the Internet, a local area network (LAN), awide area network (WAN), or any combinations thereof.

The term “computing device,” as used herein, refers to a system and/or adevice, such as a computing apparatus, that includes a capability toprocess (e.g., perform computations) and/or store content, such asmeasurements, text, images, video, audio, etc. in the form of signalsand/or states. Thus, a computing device, in this context, may comprisehardware, software, firmware, or any combination thereof (other thansoftware per se). Computing device 404, as depicted in FIG. 4, is merelyone example, and claimed subject matter is not limited in scope to thisparticular example. For one or more embodiments, a computing device maycomprise any of a wide range of digital electronic devices, including,but not limited to, personal desktop and/or notebook computers,high-definition televisions, digital versatile disc (DVD) players and/orrecorders, game consoles, satellite television receivers, cellulartelephones, wearable devices, personal digital assistants, mobile audioand/or video playback and/or recording devices, or any combination ofthe above. Further, unless specifically stated otherwise, a process asdescribed herein, with reference to flow diagrams and/or otherwise, mayalso be executed and/or affected, in whole or in part, by a computingplatform.

Memory 422 may store cookies relating to one or more users and may alsocomprise a computer-readable medium that may carry and/or makeaccessible content, including code and/or instructions, for example,executable by processor 420 and/or some other unit, such as a controllerand/or processor, capable of executing instructions, for example. A usermay make use of an input device, such as a computer mouse, stylus, trackball, keyboard, and/or any other similar device capable of receivinguser actions and/or motions as input signals. Likewise, a user may makeuse of an output device, such as a display, a printer, etc., and/or anyother device capable of providing signals and/or generating stimuli fora user, such as visual stimuli, audio stimuli and/or other similarstimuli.

Regarding aspects related to a communications and/or computing network,a wireless network may couple client devices with a network. A wirelessnetwork may employ stand-alone ad-hoc networks, mesh networks, WirelessLAN (WLAN) networks, cellular networks, and/or the like. A wirelessnetwork may further include a system of terminals, gateways, routers,and/or the like coupled by wireless radio links, and/or the like, whichmay move freely, randomly and/or organize themselves arbitrarily, suchthat network topology may change, at times even rapidly. A wirelessnetwork may further employ a plurality of network access technologies,including Long Term Evolution (LTE), WLAN, Wireless Router (WR) mesh,2nd, 3rd, or 4th generation (2G, 3G, or 4G) cellular technology and/orthe like. Network access technologies may enable wide area coverage fordevices, such as client devices with varying degrees of mobility, forexample.

A network may enable radio frequency and/or other wireless typecommunications via a wireless network access technology and/or airinterface, such as Global System for Mobile communication (GSM),Universal Mobile Telecommunications System (UMTS), General Packet RadioServices (GPRS), Enhanced Data GSM Environment (EDGE), 3GPP Long TermEvolution (LTE), LTE Advanced, Wideband Code Division Multiple Access(WCDMA), Bluetooth, ultra wideband (UWB), 802.11b/g/n, and/or the like.A wireless network may include virtually any type of now known and/or tobe developed wireless communication mechanism by which signals may becommunicated between devices, between networks, within a network, and/orthe like.

Communications between a computing device and/or a network device and awireless network may be in accordance with known and/or to be developedcommunication network protocols including, for example, global systemfor mobile communications (GSM), enhanced data rate for GSM evolution(EDGE), 802.11b/g/n, and/or worldwide interoperability for microwaveaccess (WiMAX). A computing device and/or a networking device may alsohave a subscriber identity module (SIM) card, which, for example, maycomprise a detachable smart card that is able to store subscriptioncontent of a user, and/or is also able to store a contact list of theuser. A user may own the computing device and/or networking device ormay otherwise be a user, such as a primary user, for example. Acomputing device may be assigned an address by a wireless networkoperator, a wired network operator, and/or an Internet Service Provider(ISP). For example, an address may comprise a domestic or internationaltelephone number, an Internet Protocol (IP) address, and/or one or moreother identifiers. In other embodiments, a communication network may beembodied as a wired network, wireless network, or any combinationsthereof.

A device, such as a computing and/or networking device, may vary interms of capabilities and/or features. Claimed subject matter isintended to cover a wide range of potential variations. For example, adevice may include a numeric keypad and/or other display of limitedfunctionality, such as a monochrome liquid crystal display (LCD) fordisplaying text, for example. In contrast, however, as another example,a web-enabled device may include a physical and/or a virtual keyboard,mass storage, one or more accelerometers, one or more gyroscopes, globalpositioning system (GPS) and/or other location-identifying typecapability, and/or a display with a higher degree of functionality, suchas a touch-sensitive color 2D or 3D display, for example.

A computing and/or network device may include and/or may execute avariety of now known and/or to be developed operating systems,derivatives and/or versions thereof, including personal computeroperating systems, such as a Windows, iOS, Linux, a mobile operatingsystem, such as iOS, Android, Windows Mobile, and/or the like. Acomputing device and/or network device may include and/or may execute avariety of possible applications, such as a client software applicationenabling communication with other devices, such as communicating one ormore messages, such as via protocols suitable for transmission of email,short message service (SMS), and/or multimedia message service (MMS),including via a network, such as a social network including, but notlimited to, Facebook, Linked In, Twitter, Flickr, and/or Google-F, toprovide a few examples. A computing and/or network device may alsoinclude and/or execute a software application to communicate content,such as, for example, textual content, multimedia content, and/or thelike. A computing and/or network device may also include and/or executea software application to perform a variety of possible tasks, such asbrowsing, searching, playing various forms of content, including locallystored and/or streamed video, and/or games such as, but not limited to,fantasy sports leagues. The foregoing is provided merely to illustratethat claimed subject matter is intended to include a wide range ofpossible features and/or capabilities.

A network may also be extended to another device communicating as partof another network, such as via a virtual private network (VPN). Tosupport a VPN, broadcast domain signal transmissions may be forwarded tothe VPN device via another network. For example, a software tunnel maybe created between a logical broadcast domain, and a VPN device.Tunneled traffic may, or may not be encrypted, and a tunneling protocolmay be substantially compliant with and/or substantially compatible withany now known and/or to be developed versions of any of the followingprotocols: IPSec, Transport Layer Security, Datagram Transport LayerSecurity, Microsoft Point-to-Point Encryption, Microsoft's Secure SocketTunneling Protocol, Multipath Virtual Private Network, Secure Shell VPN,another existing protocol, and/or another protocol that may bedeveloped.

A network may communicate via signal packets and/or frames, such as in anetwork of participating digital communications. A broadcast domain maybe compliant and/or compatible with, but is not limited to, now knownand/or to be developed versions of any of the following network protocolstacks: ARCNET, AppleTalk, ATM, Bluetooth, DECnet, Ethernet, FDDI, FrameRelay, HIPPI, IEEE 1394, IEEE 802.11, IEEE-488, Internet Protocol Suite,IPX, Myrinet, OSI Protocol Suite, QsNet, RS-232, SPX, System NetworkArchitecture, Token Ring, USB, and/or X.25. A broadcast domain mayemploy, for example, TCP/IP, UDP, DECnet, NetBEUI, IPX, Appletalk,other, and/or the like. Versions of the Internet Protocol (IP) mayinclude IPv4, IPv6, other, and/or the like.

Algorithmic descriptions and/or symbolic representations are examples oftechniques used by those of ordinary skill in the signal processingand/or related arts to convey the substance of their work to othersskilled in the art. An algorithm is here, and generally, is consideredto be a self-consistent sequence of operations and/or similar signalprocessing leading to a desired result. In this context, operationsand/or processing involve physical manipulation of physical quantities.Typically, although not necessarily, such quantities may take the formof electrical and/or magnetic signals and/or states capable of beingstored, transferred, combined, compared, processed or otherwisemanipulated as electronic signals and/or states representing variousforms of content, such as signal measurements, text, images, video,audio, etc. It has proven convenient at times, principally for reasonsof common usage, to refer to such physical signals and/or physicalstates as bits, values, elements, symbols, characters, terms, numbers,numerals, measurements, content and/or the like. It should beunderstood, however, that all of these and/or similar terms are to beassociated with appropriate physical quantities and are merelyconvenient labels. Unless specifically stated otherwise, as apparentfrom the preceding discussion, it is appreciated that throughout thisspecification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining”, “establishing”, “obtaining”,“identifying”, “selecting”, “generating”, and/or the like may refer toactions and/or processes of a specific apparatus, such as a specialpurpose computer and/or a similar special purpose computing and/ornetwork device. In the context of this specification, therefore, aspecial purpose computer and/or a similar special purpose computingand/or network device is capable of processing, manipulating and/ortransforming signals and/or states, typically represented as physicalelectronic and/or magnetic quantities within memories, registers, and/orother storage devices, transmission devices, and/or display devices ofthe special purpose computer and/or similar special purpose computingand/or network device. In the context of this particular patentapplication, as mentioned, the term “specific apparatus” may include ageneral purpose computing and/or network device, such as a generalpurpose computer, once it is programmed to perform particular functionspursuant to instructions from program software.

In some circumstances, operation of a memory device, such as a change instate from a binary one to a binary zero or vice-versa, for example, maycomprise a transformation, such as a physical transformation. Withparticular types of memory devices, such a physical transformation maycomprise a physical transformation of an article to a different state orthing. For example, but without limitation, for some types of memorydevices, a change in state may involve an accumulation and/or storage ofcharge or a release of stored charge. Likewise, in other memory devices,a change of state may comprise a physical change, such as atransformation in magnetic orientation and/or a physical change and/ortransformation in molecular structure, such as from crystalline toamorphous or vice-versa. In still other memory devices, a change inphysical state may involve quantum mechanical phenomena, such as,superposition, entanglement, and/or the like, which may involve quantumbits (qubits), for example. The foregoing is not intended to be anexhaustive list of all examples in which a change in state form a binaryone to a binary zero or vice-versa in a memory device may comprise atransformation, such as a physical transformation. Rather, the foregoingis intended as illustrative examples.

In the preceding description, various aspects of claimed subject matterhave been described. For purposes of explanation, specifics, such asamounts, systems and/or configurations as examples, were set forth. Inother instances, well-known features were omitted and/or simplified soas not to obscure claimed subject matter. While certain features havebeen illustrated and/or described herein, many modifications,substitutions, changes and/or equivalents will now occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all modifications and/or changes as fallwithin claimed subject matter.

What is claimed is:
 1. A method comprising: omitting, from beingrecommended, online content to one or more users in an online socialnetwork based at least in part on an estimate that the one or more usersare more likely than not to view the online content via a differentsource.
 2. The method of claim 1, wherein the online content comprisesone or more of the following: a video; an image; a web page; an article;a product description; a service description; an advertisement; orcombinations thereof.
 3. The method of claim 1, wherein the estimate isbased at least in part on a prediction of diffusion of the onlinecontent through the social network.
 4. The method of claim 3, whereinthe prediction of diffusion is based at least in part on one or more ofthe following factors: online content retransmission probability; numberof successive retransmissions; distance from online content source in asocial network graph; relevance of online content to users; orcombinations thereof.
 5. The method of claim 1, wherein the predictionof diffusion of the online content into the social network is based, atleast in part on, generating a set of users reachable from a user in amajority of social network graphs.
 6. The method of claim 1, wherein theprediction of diffusion of the online content into the social network isbased at least in part, on generating a set of users with a path lengthfrom the source less than a threshold repost distance.
 7. The method ofclaim 1, wherein the prediction of diffusion of the online content intothe social network is based at least in part, on generating a set ofusers who have retransmission probabilities greater than a thresholdretransmission probability.
 8. The method of claim 1, wherein theestimate that the one or more users are more likely than not to view theonline content via a different source is based, at least in part, onusers having an increased online content diffusion relative to otherusers.
 9. The method of claim 1, wherein the estimate that the one ormore users are more likely than not to view the online content via adifferent source is based, at least in part, on users having anincreased number of linked neighbors.
 10. The method of claim 1, furthercomprising recommending non-omitted online content to users based, atleast in part, on predicted engagement weight of the online contentand/or whether a threshold number of recommended online content has beenexceeded relative to other users,
 11. A system comprising: a computingdevice to omit, from being recommended, online content to one or moreusers in an online social network based at least hi part on an estimatethat the one or more users are more likely than not to view the onlinecontent via a different source.
 12. The system of claim 11, wherein theonline content to comprise one or more of the following: a video; animage; a web page; an article: a product description; a servicedescription; an advertisement; or combinations thereof.
 13. The systemof claim 11, the computing device further to predict diffusion of theonline content through the social network
 14. The system of claim 13,wherein the diffusion prediction is to be based at least in part on oneor more of the following factors: online content retransmissionprobability; number of successive retransmissions; distance from anonline content source in a social network graph; relevance of onlinecontent to users: or combination thereof.
 15. The system of claim 11 thecomputing device to further recommend non-omitted online content tousers to be based, at least in part, on predicted engagement weight ofthe online content.
 16. An apparatus comprising: means for omitting,from being recommended, online content to one or more users in an onlinesocial network based at least in part on an estimate that the one ormore users are more likely than not to view the online content via adifferent source.
 17. The apparatus of claim 16, wherein the onlinecontent comprises one or more of the following: a video; an image; a webpage; an article: a product description; a service description; anadvertisement; or combinations thereof.
 18. The apparatus of claim 16,the means for estimating further comprises means for predictingdiffusion of the online content through the social network.
 19. Theapparatus of claim 18, wherein the prediction of diffusion is based atleast in part on one or more of the following factors: online contentretransmission probability; number of successive retransmissions;distance from an online content source in a sodas network graph;relevance of online content to users; or combination thereof.
 20. Theapparatus of claim 16, further comprising means for recommendingnon-omitted online content to users based, at least in part, onpredicted engagement weight of the online content.